From c98be6d55b77929c88f91aa42401b9bd89bc7601 Mon Sep 17 00:00:00 2001 From: Mary Llewellyn Date: Mon, 26 Feb 2024 11:35:22 +0000 Subject: [PATCH] address task 15, issue /issues/112 --- episodes/01-introduction-to-high-dimensional-data.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/episodes/01-introduction-to-high-dimensional-data.Rmd b/episodes/01-introduction-to-high-dimensional-data.Rmd index 15041942..a3564a77 100644 --- a/episodes/01-introduction-to-high-dimensional-data.Rmd +++ b/episodes/01-introduction-to-high-dimensional-data.Rmd @@ -55,8 +55,8 @@ pose a challenge for data analysis as standard methods of analysis, such as line regression, are no longer appropriate. High-dimensional datasets are common in the biological sciences. Subjects like -genomics and medical sciences often use both tall (in terms of $n$) and wide -(in terms of $p$) datasets that can be difficult to analyse or visualise using +genomics and medical sciences often use both tall (large $n$) and wide +(large $p$) datasets, which can be difficult to analyse or visualise using standard statistical tools. An example of high-dimensional data in biological sciences may include data collected from hospital patients recording symptoms, blood test results, behaviours, and general health, resulting in datasets with